Methods and systems for assessing image quality in modeling of patient anatomic or blood flow characteristics

ABSTRACT

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient&#39;s anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient&#39;s anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient&#39;s anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient&#39;s anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

PRIORITY

This application is a continuation of U.S. patent application Ser. No.14/484,112, filed Sep. 11, 2014, which is a continuation of U.S. patentapplication Ser. No. 14/172,554, filed Feb. 4, 2014 (now U.S. Pat. No.8,861,820), which is a continuation of U.S. application Ser. No.14/163,589, filed Jan. 24, 2014, (now U.S. Pat. No. 8,824,752), issuedSep. 2, 2014, which claims the benefit of priority from U.S. ProvisionalApplication No. 61/793,162, filed Mar. 15, 2013, each of which is hereinincorporated by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate to methods and systems forassessing medical image quality and, more particularly, to methods andsystems for assessing medical image quality in relation topatient-specific modeling of anatomy and/or blood flow.

BACKGROUND

Medical imaging is an important technology used to gain anatomic andphysiologic data about a patient's body, organs, tissues, or a portionthereof for clinical diagnosis and treatment planning. Medical imagingincludes, but is not limited to, radiography, computed tomography (CT),magnetic resonance imaging (MRI), fluoroscopy, single-photon emissioncomputed tomography (SPECT), positron emission tomography (PET),scintigraphy, ultrasound, and specific techniques such asechocardiography, mammography, intravascular ultrasound, andangiography. Imaging data may be obtained through non-invasive orinvasive procedures. The fields of cardiology, neuroscience, oncology,orthopedics, and many others benefit from information obtained inmedical imaging.

In the field of cardiology, in particular, it is well known thatcoronary artery disease may cause the blood vessels providing blood tothe heart to develop lesions, such as a stenosis (abnormal narrowing ofa blood vessel). As a result, blood flow to the heart may be restricted.A patient suffering from coronary artery disease may experience chestpain, referred to as chronic stable angina during physical exertion orunstable angina when the patient is at rest. A more severe manifestationof disease may lead to myocardial infarction, or heart attack. A needexists to provide more accurate data relating to coronary lesions, e.g.,size, shape, location, functional significance (e.g., whether the lesionimpacts blood flow), etc. Patients suffering from chest pain and/orexhibiting symptoms of coronary artery disease may be subjected to oneor more tests, such as based on medical imaging, that may provide someindirect evidence relating to coronary lesions.

In addition to CT, SPECT, and PT, the use of medical imaging fornoninvasive coronary evaluation may include electrocardiograms,biomarker evaluation from blood tests, treadmill tests, andechocardiography. These noninvasive tests, however, typically do notprovide a direct assessment of coronary lesions or assess blood flowrates. The noninvasive tests may provide indirect evidence of coronarylesions by looking for changes in electrical activity of the heart(e.g., using electrocardiography (ECG)), motion of the myocardium (e.g.,using stress echocardiography), perfusion of the myocardium (e.g., usingPET or SPECT), or metabolic changes (e.g., using biomarkers).

For example, anatomic data may be obtained noninvasively using coronarycomputed tomographic angiography (CCTA). CCTA may be used for imaging ofpatients with chest pain and involves using CT technology to image theheart and the coronary arteries following an intravenous infusion of acontrast agent. However, CCTA also cannot provide direct information onthe functional significance of coronary lesions, e.g., whether thelesions affect blood flow. In addition, since CCTA is purely adiagnostic test, it can neither be used to predict changes in coronaryblood flow, pressure, or myocardial perfusion under other physiologicstates (e.g., exercise), nor can it be used to predict outcomes ofinterventions.

Thus, patients may require an invasive test, such as diagnostic cardiaccatheterization, to visualize coronary lesions. Diagnostic cardiaccatheterization may include performing conventional coronary angiography(CCA) to gather anatomic data on coronary lesions by providing a doctorwith an image of the size and shape of the arteries. CCA, however, doesnot provide data for assessing the functional significance of coronarylesions. For example, a doctor may not be able to diagnose whether acoronary lesion is harmful without determining whether the lesion isfunctionally significant. Thus, CCA has led to a procedure referred toas an “oculostenotic reflex,” in which interventional cardiologistsinsert a stent for every lesion found with CCA regardless of whether thelesion is functionally significant. As a result, CCA may lead tounnecessary operations on the patient, which may pose added risks topatients and may result in unnecessary heath care costs for patients.

During diagnostic cardiac catheterization, the functional significanceof a coronary lesion may be assessed invasively by measuring thefractional flow reserve (FFR) of an observed lesion. FFR is defined asthe ratio of the mean blood pressure downstream of a lesion divided bythe mean blood pressure upstream from the lesion, e.g., the aorticpressure, under conditions of increased coronary blood flow, e.g., wheninduced by intravenous administration of adenosine. Blood pressures maybe measured by inserting a pressure wire into the patient. Thus, thedecision to treat a lesion based on the determined FFR may be made afterthe initial cost and risk of diagnostic cardiac catheterization hasalready been incurred.

To fill the gaps left by each of the pure medical imaging and invasiveprocedures described above, HeartFlow, Inc. has developed simulation andmodeling technology based on patient-specific imaging data. For example,various simulation, modeling, and computational techniques include, butare not limited to: computational mechanics, computational fluiddynamics (CFD), numerical simulation, multi-scale modeling, monte carlosimulation, machine learning, artificial intelligence and various othercomputational methods to solve mathematical models. These techniques mayprovide information about biomechanics, fluid mechanics, changes toanatomy and physiology over time, electrophysiology, stresses andstrains on tissue, organ function, and neurologic function, amongothers. This information may be provided at the time of the imagingstudy or prediction of changes over time as a result of medicalprocedures or the passage of time and progression of disease.

One illustrative application of computational simulation and modeling isdescribed by HeartFlow, Inc., for modeling vascular blood flow fromnon-invasive imaging data, including assessing the effect of variousmedical, interventional, or surgical treatments (see, e.g., U.S. Pat.Nos. 8,386,188; 8,321,150; 8,315,814; 8,315,813; 8,315,812; 8,311,750;8,311,748; 8,311,747; and 8,157,742). In particular, HeartFlow, Inc. hasdeveloped methods for assessing coronary anatomy, myocardial perfusion,and coronary artery flow, noninvasively, to reduce the abovedisadvantages of invasive FFR measurements. Specifically, CFDsimulations have been successfully used to predict spatial and temporalvariations of flow rate and pressure of blood in arteries, includingFFR. Such methods and systems benefit cardiologists who diagnose andplan treatments for patients with suspected coronary artery disease, andpredict coronary artery flow and myocardial perfusion under conditionsthat cannot be directly measured, e.g., exercise, and to predictoutcomes of medical, interventional, and surgical treatments on coronaryartery blood flow and myocardial perfusion.

For the above-described techniques, and many other applications ofimage-based modeling and simulation, the characteristics and quality ofthe image data is important. During acquisition of medical imaging data,a variety of artifacts or limitations may exist that affect the qualityof the image. For example, settings and capabilities of spatial andtemporal resolution, energy-tissue interactions, patient or organmovement, reconstruction algorithms, hardware failures, timing oracquisition, detector sensitivity, medication or contrast mediaadministered, patient preparation, and various other factors can affectthe resulting image quality. Effects include, but are not limited to,poor resolution, motion or blurring artifacts, high noise, low contrastof tissue, poor perfusion, partial volume effect, distortion, clippingof structures, shadowing, etc. Since these quality issues may affect theperformance and accuracy of models and simulations based on the imagingdata, there is a need to determine if image quality is suitable or todetermine the effect of image quality on modeling and simulationresults.

As a result, there is a need for methods and systems for assessing andquantifying medical image quality and, more particularly, to methods andsystems for assessing and quantifying medical image quality in relationto patient-specific modeling of blood flow. The foregoing generaldescription and the following detailed description are exemplary andexplanatory only and are not restrictive of the disclosure.

SUMMARY

In accordance with an embodiment, methods are disclosed for assessingthe quality of medical images of at least a portion of a patient'sanatomy, using a computer system. One method includes receiving one ormore images of at least a portion of the patient's anatomy; determining,using a processor of the computer system, one or more image propertiesof the received images; performing, using a processor of the computersystem, anatomic localization or modeling of at least a portion of thepatient's anatomy based on the received images; obtaining anidentification of one or more image characteristics associated with ananatomic feature of the patient's anatomy based on the anatomiclocalization or modeling; and calculating, using a processor of thecomputer system, an image quality score based on the one or more imageproperties and the one or more image characteristics.

In accordance with another embodiment, systems are disclosed forassessing the quality of medical images of at least a portion of apatient's anatomy. One system includes a digital storage device storinginstructions for assessing the quality of medical images of at least aportion of a patient's anatomy; and a processor configured to executethe instructions to perform a method including: receiving one or moreimages of at least a portion of the patient's anatomy; determining,using a processor of the computer system, one or more image propertiesof the received images; performing, using a processor of the computersystem, anatomic localization or modeling of at least a portion of thepatient's anatomy based on the received images; obtaining anidentification of one or more image characteristics associated with ananatomic feature of the patient's anatomy based on the anatomiclocalization or modeling; and calculating, using a processor of thecomputer system, an image quality score based on the one or more imageproperties and the one or more image characteristics.

In accordance with another embodiment, a non-transitory computerreadable medium is disclosed for use on at least one computer systemcontaining computer-executable programming instructions for assessingthe quality of medical images of at least a portion of a patient'sanatomy, that when executed by the at least one computer system, causethe performance of a method comprising: receiving one or more images ofat least a portion of the patient's anatomy; determining, using aprocessor of the computer system, one or more image properties of thereceived images; performing, using a processor of the computer system,anatomic localization or modeling of at least a portion of the patient'sanatomy based on the received images; obtaining an identification of oneor more image characteristics associated with an anatomic feature of thepatient's vasculature based on the anatomic localization or modeling;and calculating, using a processor of the computer system, an imagequality score based on the one or more image properties and the one ormore image characteristics.

Additional embodiments and advantages will be set forth in part in thedescription which follows, and in part will be obvious from thedescription, or may be learned by practice of the disclosure. Theembodiments and advantages will be realized and attained by means of theelements and combinations particularly pointed out below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments and togetherwith the description, serve to explain the principles of the disclosure.

FIG. 1 is a schematic diagram of a system for determining variousinformation relating to coronary blood flow in a specific patient,according to an exemplary embodiment;

FIG. 2 is a flow chart of a method for determining various informationrelating to coronary blood flow in a specific patient, according to anexemplary embodiment;

FIG. 3 is a flow chart that describes an exemplary method for assessingmedical image quality, generating image quality metrics, and using imagequality metrics, according to various exemplary embodiments;

FIG. 4 is a flow chart that describes an exemplary method for enablingand performing user-guided assessment of medical image quality,according to an exemplary embodiment;

FIG. 5 is a flow chart that describes an exemplary process forperforming computer-automated assessment of medical image quality,generation of image quality metrics, and use of image quality metrics,according to various exemplary embodiments;

FIG. 6 is a flow chart that describes an exemplary process for assessingmedical image quality, generating image quality metrics, and using imagequality metrics, in the context of estimating coronary fractional flowreserve values, according to various exemplary embodiments;

FIG. 7A is an exemplary box plot of fractional flow reserve error andacceptance or rejection based on CT image quality review, according tovarious exemplary embodiments;

FIG. 7B is an exemplary box plot of fractional flow reserve error andscoring based on CT image quality review, according to various exemplaryembodiments;

FIG. 8 is an exemplary bar graph depicting comparisons betweenperformance or accuracy of fractional flow reserve and computedtomography, based on image quality, by number of vessels, according tovarious exemplary embodiments;

FIG. 9 is a table depicting an exemplary rubric for scoring imagecharacteristics based on lumen features of cardiovascular vessels,according to various exemplary embodiments; and

FIG. 10 is a screenshot of an exemplary interface for displayingcomputed tomography quality ratings vs. benchmark performance, accordingto various exemplary embodiments.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

Overview

The present disclosure relates to assessing and quantifying the qualityof medical images. In one embodiment, the present disclosure describessystems and methods for assessing image quality for the purpose ofpredicting or analyzing the accuracy and performance of medical imagerysimulation and modeling. In one embodiment, a method of assessingmedical image quality includes: receiving image data and possiblypatient information; performing assessment of image quality by computerautomated, user-guided, or a combination of means at a local and/orglobal level; and generating image quality metrics that are regional(e.g., for a vessel) or for an entire dataset or multiple datasets. Inone embodiment, a method of assessing medical image quality may includeapplying image quality metrics for one or more of: (i) evaluatingwhether imaging data is suitable to achieve desired simulation accuracy,precision, and/or performance; (ii) estimating the accuracy, precision,or confidence of simulation results; (iii) guiding simulation ormodeling techniques best suited to achieve desired accuracy, precision,and/or performance; and/or (iv) selecting, combining, or correcting thebest data from a variety of received data in order to achieve desiredaccuracy, precision, and/or performance.

In one embodiment, quality issues or anomalies may include, but are notlimited to, low contrast, noise, motion or blurring, misregistration ormisalignment, low resolution, partial volume effect, beam hardening,clipped anatomy excluded from the scan, streaking, scanner failures,missing data, and/or inconsistent contrast timing. If these issuesaffect information of interest, such as the anatomy of coronaryarteries, in such a manner that they may affect the quality, accuracy,or performance of blood flow models and simulations, then it may bedesirable to detect and score the image quality issues. Then, thequality of the imaging data may be analyzed for its effect on theability to extract the desired information from patient images.

In an exemplary embodiment, the disclosed methods and systems involvethe use of at least one computer configured to receive patient-specificimaging data containing at least some of the coronary vasculature. Inorder to model coronary blood flow from imaging data, at least someportions of the coronary artery anatomy may be measured, modeled,segmented, or estimated. Additionally, at least some portions of theheart, aorta, myocardium, ventricles, valves, veins and other structuresof the heart may be measured, modeled, segmented, or estimated. Alongwith anatomic representations, information regarding contrast levels,contrast gradients, or other image-analysis metrics may be extracted toinform the model.

Thus, in such an exemplary embodiment, methods and systems are disclosedfor determining image quality from patient-specific imaging data for thepurposes of blood flow modeling and simulation. Such an embodiment mayinclude the assessment of coronary computed topographic angiography(cCTA) imaging data to simulate information including, but not limitedto, coronary blood flow, velocity, pressure, plaque and wall stress, andfractional flow reserve (FFR). The methods and systems may be adopted toother areas of the vasculature including, but not limited to, carotid,peripheral, abdominal, renal, and cerebral, as well as to other imagingmodalities including, but not limited to, MRI, PET, SPECT, ultrasound,and angiography.

Accordingly, in certain embodiments that follow, systems and methods forassessing and quantifying image quality are described, for purposes ofexample, in the context of images of coronary vasculature. Morespecifically, in certain embodiments, systems and methods for assessingand quantifying image quality are described, for purposes of example, inthe context of analyzing the quality of images used in modelingpatient-specific coronary vasculature, and simulating blood flow throughpatient-specific coronary vasculature. However, it should be appreciatedthat the presently disclosed techniques for assessing and quantifyingimage quality are equally applicable to evaluating and manipulatingmedical imagery in relation to any anatomy, or in relation to anycardiovascular evaluation, among any other medical diagnostictechniques.

Exemplary Cardiovascular Context

In one embodiment, the present disclosure relates to methods and systemsfor assessing image quality in the context of determining blood flowinformation in a specific patient, using information retrieved from thepatient noninvasively. Various embodiments of such a method and systemare described in greater detail in U.S. Pat. No. 8,315,812, filed Jan.25, 2011, and entitled “Method and System for Patient-Specific Modelingof Blood Flow,” which is hereby incorporated by reference in itsentirety.

In some embodiments, the information determined by the method and systemmay relate to blood flow in the patient's coronary vasculature.Alternatively, the determined information may relate to blood flow inother areas of the patient's vasculature, such as carotid, peripheral,abdominal, renal, and cerebral vasculature. The coronary vasculatureincludes a complex network of vessels ranging from large arteries toarterioles, capillaries, venules, veins, etc. The coronary vasculaturecirculates blood to and within the heart and includes an aorta thatsupplies blood to a plurality of main coronary arteries (e.g., the leftanterior descending (LAD) artery, the left circumflex (LCX) artery, theright coronary (RCA) artery, etc.), which may further divide intobranches of arteries or other types of vessels downstream from the aortaand the main coronary arteries. Thus, the exemplary method and systemmay determine information relating to blood flow within the aorta, themain coronary arteries, and/or other coronary arteries or vesselsdownstream from the main coronary arteries. Although the aorta andcoronary arteries (and the branches that extend therefrom) are discussedbelow, the disclosed method and system may also apply to other types ofvessels.

In an exemplary embodiment, the information determined by the disclosedmethods and systems may include, but is not limited to, various bloodflow characteristics or parameters, such as blood flow velocity,pressure (or a ratio thereof), flow rate, and FFR at various locationsin the aorta, the main coronary arteries, and/or other coronary arteriesor vessels downstream from the main coronary arteries. This informationmay be used to determine whether a lesion is functionally significantand/or whether to treat the lesion. This information may be determinedusing information obtained noninvasively from the patient. As a result,the decision whether to treat a lesion may be made without the cost andrisk associated with invasive procedures.

FIG. 1 shows aspects of a system for providing information relating tocoronary blood flow in a specific patient, according to an exemplaryembodiment. A three-dimensional model 10 of the patient's anatomy may becreated using data obtained noninvasively from the patient as will bedescribed below in more detail. Other patient-specific information mayalso be obtained noninvasively. In an exemplary embodiment, the portionof the patient's anatomy that is represented by the three-dimensionalmodel 10 may include at least a portion of the aorta and a proximalportion of the main coronary arteries (and the branches extending oremanating therefrom) connected to the aorta.

Various physiological laws or relationships 20 relating to coronaryblood flow may be deduced, e.g., from experimental data as will bedescribed below in more detail. Using the three-dimensional anatomicalmodel 10 and the deduced physiological laws 20, a plurality of equations30 relating to coronary blood flow may be determined as will bedescribed below in more detail. For example, the equations 30 may bedetermined and solved using any numerical method, e.g., finitedifference, finite volume, spectral, lattice Boltzmann, particle-based,level set, finite element methods, etc. The equations 30 can be solvedto determine information (e.g., pressure, velocity, FFR, etc.) about thecoronary blood flow in the patient's anatomy at various points in theanatomy represented by the model 10.

The equations 30 may be solved using a computer system 40. Based on thesolved equations, the computer system 40 may output one or more imagesor simulations indicating information relating to the blood flow in thepatient's anatomy represented by the model 10. For example, the image(s)may include a simulated blood pressure model 50, a simulated blood flowor velocity model 52, a computed FFR (cFFR) model 54, etc., as will bedescribed in further detail below. The simulated blood pressure model50, the simulated blood flow model 52, and the cFFR model 54 provideinformation regarding the respective pressure, velocity, and cFFR atvarious locations along three dimensions in the patient's anatomyrepresented by the model 10. cFFR may be calculated as the ratio of theblood pressure at a particular location in the model 10 divided by theblood pressure in the aorta, e.g., at the inflow boundary of the model10, under conditions of increased coronary blood flow, e.g.,conventionally induced by intravenous administration of adenosine.

In an exemplary embodiment, the computer system 40 may include one ormore non-transitory computer-readable storage devices that storeinstructions that, when executed by a processor, computer system, etc.,may perform any of the actions described herein for providing varioussources of information relating to blood flow in the patient. Thecomputer system 40 may include a desktop or portable computer, aworkstation, a server, a personal digital assistant, or any othercomputer system. The computer system 40 may include a processor, aread-only memory (ROM), a random access memory (RAM), an input/output(I/O) adapter for connecting peripheral devices (e.g., an input device,output device, storage device, etc.), a user interface adapter forconnecting input devices such as a keyboard, a mouse, a touch screen, avoice input, and/or other devices, a communications adapter forconnecting the computer system 40 to a network, a display adapter forconnecting the computer system 40 to a display, etc. For example, thedisplay may be used to display the three-dimensional model 10 and/or anyimages generated by solving the equations 30, such as the simulatedblood pressure model 50, the simulated blood flow model 52, and/or thecFFR model 54.

FIG. 2 shows aspects of a method 60 for providing various sources ofinformation relating to blood flow in a specific patient, according toanother exemplary embodiment. The method may include obtainingpatient-specific anatomical data, such as information regarding thepatient's anatomy (e.g., at least a portion of the aorta and a proximalportion of the main coronary arteries (and the branches extendingtherefrom) connected to the aorta), and preprocessing the data (step62). The patient-specific anatomical data may be obtained noninvasively,e.g., by CCTA.

A three-dimensional model of the patient's anatomy may be created basedon the obtained anatomical data (step 64). For example, thethree-dimensional model may be the three-dimensional model 10 of thepatient's anatomy described above in connection with FIG. 1.

The three-dimensional model may be prepared for analysis and boundaryconditions may be determined (step 66). For example, thethree-dimensional model 10 of the patient's anatomy described above inconnection with FIG. 1 may be trimmed and discretized into a volumetricmesh, e.g., a finite element or finite volume mesh. The volumetric meshmay be used to generate the equations 30 described above in connectionwith FIG. 1.

Boundary conditions may also be assigned and incorporated into theequations 30 described above in connection with FIG. 1. The boundaryconditions provide information about the three-dimensional model 10 atits boundaries, e.g., inflow boundaries, outflow boundaries, vessel wallboundaries, etc. The inflow boundaries may include the boundariesthrough which flow is directed into the anatomy of the three-dimensionalmodel, such as at an end of the aorta near the aortic root. Each inflowboundary may be assigned, e.g., with a prescribed value or field forvelocity, flow rate, pressure, or other characteristic, by coupling aheart model and/or a lumped parameter model to the boundary, etc. Theoutflow boundaries may include the boundaries through which flow isdirected outward from the anatomy of the three-dimensional model, suchas at an end of the aorta near the aortic arch, and the downstream endsof the main coronary arteries and the branches that extend therefrom.Each outflow boundary can be assigned, e.g., by coupling a lumpedparameter or distributed (e.g., a one-dimensional wave propagation)model. The prescribed values for the inflow and/or outflow boundaryconditions may be determined by noninvasively measuring physiologiccharacteristics of the patient, such as, but not limited to, cardiacoutput (the volume of blood flow from the heart), blood pressure,myocardial mass, etc. The vessel wall boundaries may include thephysical boundaries of the aorta, the main coronary arteries, and/orother coronary arteries or vessels of the three-dimensional model 10.

The computational analysis may be performed using the preparedthree-dimensional model and the determined boundary conditions (step 68)to determine blood flow information for the patient. For example, thecomputational analysis may be performed with the equations 30 and usingthe computer system 40 described above in connection with FIG. 1 toproduce the images described above in connection with FIG. 1, such asthe simulated blood pressure model 50, the simulated blood flow model52, and/or the cFFR model 54.

The method may also include providing patient-specific treatment optionsusing the results (step 70). For example, the three-dimensional model 10created in step 64 and/or the boundary conditions assigned in step 66may be adjusted to model one or more treatments, e.g., placing acoronary stent in one of the coronary arteries represented in thethree-dimensional model 10 or other treatment options. Then, thecomputational analysis may be performed as described above in step 68 inorder to produce new images, such as updated versions of the bloodpressure model 50, the blood flow model 52, and/or the cFFR model 54.These new images may be used to determine a change in blood flowvelocity and pressure if the treatment option(s) are adopted.

The systems and methods disclosed herein may be incorporated into asoftware tool accessed by physicians to provide a noninvasive means toquantify blood flow in the coronary arteries and to assess thefunctional significance of coronary artery disease. In addition,physicians may use the software tool to predict the effect of medical,interventional, and/or surgical treatments on coronary artery bloodflow. The software tool may prevent, diagnose, manage, and/or treatdisease in other portions of the cardiovascular system includingarteries of the neck (e.g., carotid arteries), arteries in the head(e.g., cerebral arteries), arteries in the thorax, arteries in theabdomen (e.g., the abdominal aorta and its branches), arteries in thearms, or arteries in the legs (e.g., the femoral and poplitealarteries). The software tool may be interactive to enable physicians todevelop optimal personalized therapies for patients.

For example, the software tool may be incorporated at least partiallyinto a computer system, e.g., the computer system 40 shown in FIG. 1used by a physician or other user. The computer system may receive dataobtained noninvasively from the patient (e.g., data used to create thethree-dimensional model 10, data used to apply boundary conditions orperform the computational analysis, etc.). For example, the data may beinput by the physician or may be received from another source capable ofaccessing and providing such data, such as a radiology or other medicallab. The data may be transmitted via a network or other system forcommunicating the data, or directly into the computer system. Thesoftware tool may use the data to produce and display thethree-dimensional model 10 or other models/meshes and/or any simulationsor other results determined by solving the equations 30 described abovein connection with FIG. 1, such as the simulated blood pressure model50, the simulated blood flow model 52, and/or the cFFR model 54. Thus,the software tool may perform steps 62-70. In step 70, the physician mayprovide further inputs to the computer system to select possibletreatment options, and the computer system may display to the physiciannew simulations based on the selected possible treatment options.Further, each of steps 62-70 shown in FIG. 2 may be performed usingseparate software packages or modules.

Alternatively, the software tool may be provided as part of a web-basedservice or other service, e.g., a service provided by an entity that isseparate from the physician. The service provider may, for example,operate the web-based service and may provide a web portal or otherweb-based application (e.g., run on a server or other computer systemoperated by the service provider) that is accessible to physicians orother users via a network or other methods of communicating data betweencomputer systems. For example, the data obtained noninvasively from thepatient may be provided to the service provider, and the serviceprovider may use the data to produce the three-dimensional model 10 orother models/meshes and/or any simulations or other results determinedby solving the equations 30 described above in connection with FIG. 1,such as the simulated blood pressure model 50, the simulated blood flowmodel 52, and/or the cFFR model 54. Then, the web-based service maytransmit information relating to the three-dimensional model 10 or othermodels/meshes and/or the simulations so that the three-dimensional model10 and/or the simulations may be displayed to the physician on thephysician's computer system. Thus, the web-based service may performsteps 62-70 and any other steps described below for providingpatient-specific information. In step 70, the physician may providefurther inputs, e.g., to select possible treatment options or make otheradjustments to the computational analysis, and the inputs may betransmitted to the computer system operated by the service provider(e.g., via the web portal). The web-based service may produce newsimulations or other results based on the selected possible treatmentoptions, and may communicate information relating to the new simulationsback to the physician so that the new simulations may be displayed tothe physician.

Image Quality Assessment

The above-described techniques for computational modeling fornoninvasively calculating FFR may benefit from assessments of imagequality. Accordingly, the present disclosure describes methods andsystems for quantifying and assessing the effects of image quality ofthe available data on the anatomic and mathematical models used insimulating blood flow characteristics. In addition, the presentdisclosure describes methods and systems for assessing the uncertaintyof vessel and other anatomic models based on local and global imageproperties; and computing confidence intervals of simulated blood flowcalculations based on predicted uncertainty.

In an exemplary embodiment, methods and systems may implement at leastone computer configured to detect and score image quality issues. In anexemplary embodiment, coronary imaging data is analyzed by a combinationof automated and user-guided methods using at least one computer system.As will be described in more detail below, the disclosed methods andsystems may be fully automated, fully user-guided, or both automated anduser-guided. The disclosed methods and systems may be configured toperform an assessment that may include an evaluation or quantificationof one or more of the potential image quality issues listed below:

-   -   image resolution    -   slice thickness    -   reconstruction kernel    -   number of scanner slices    -   missing slices or missing data    -   phase of acquisition    -   medication provided at time of acquisition    -   heart rate at time of acquisition    -   anatomic data that is desired but not included in the image data    -   presence of anatomic abnormalities    -   presence of implanted devices or prior surgeries    -   contrast level    -   noise level    -   contrast to noise ratio    -   misregistration or misalignment    -   motion or blurring    -   partial volume effect or blooming    -   beam hardening    -   general uninterpretable or poorly defined regions

In an exemplary embodiment, these issues may be detected at a globallevel, local level, or both global and local levels. A global levelissue may involve detecting an image quality issue based on the entireimage volume, and may in some cases be referred to as an “imageproperty.” A local level issue may involve the detection space of aparticular region, e.g., around some or all of the coronary arteries,coronary plaque, along one or more vessel centerlines, etc., and may insome cases be referred to as an “image characteristic.”

In an exemplary embodiment, systems and methods for determining andassessing image quality may use a combination of automated anduser-guided quantitative and qualitative assessment of the local andglobal image quality issues, based on the previously mentioned qualityissues.

Image quality issues, such as CT imaging artifacts may come from aplurality of sources including: (i) physical-based sources, such as fromtube (kVP, mA) and photons (fluctuation, starvation), beam hardening(streaks, dark bands, etc.), partial volume (blooming), undersampling(blooming), and gantry rotation speed; (ii) patient-based sources, suchas heart rate, regular rhythm (motion), metal material, and BMI (beamhardening); (iii) scanner-based sources, such as detector array entitiesout of calibration, or reconstruction kernels and methods; and/or (iv)protocol-based sources, such as Beta blockers administration (to lowerHR), contrast agent administration (high concentration, flow rate,single, dual, triple phase), contrast timing control, etc., ECG sync andcorrection, nitroglycerin (to enlarge vessel and increaseopacification), and left vs. left+right heart opacification.

FIG. 3 is a flow chart that describes an exemplary method 100 forassessing medical image quality, generating image quality metrics, andusing image quality metrics, according to various exemplary embodiments.In one embodiment, method 100 includes receiving patient image data(step 102). Specifically, in accordance with one embodiment, step 102may include implementing at least one computer system for determiningimage quality for simulation and modeling by receiving patient-specificdata regarding the patient's body, organs, tissue, or portion thereof.For example, step 102 may include obtaining patient-specific data 10 atcomputer system 40, or any other computational system (which may be, butis not limited to: a computer, laptop, mobile phone, mobile tablet, DSP,cloud computing system, server farm, etc.).

Method 100 may include performing automated, user-guided, or combinedautomated and user-guided assessment of local and/or global quality ofthe received image data (step 104). For example, in an automatedembodiment, a computer system may automatically determine both globalquality assessments of an entire image or group of images, and localquality assessments of specific portions of a single image or portionsof a patient's imaged anatomy. In a user-guided embodiment, a computersystem may prompt a user to determine and enter global qualityassessments of an entire image or group of images, and determine localquality assessments of specific portions of a single image or portionsof a patient's imaged anatomy. In certain embodiments, certain aspectsof the local and/or global quality assessments may be formed by anycombination of automated and user-guided assessment.

The at least one computer system and method may assess or score asingle, various, or combinations of features of image quality togenerate image quality metrics for regions of interest or for an entireimage dataset (step 106). Specifically, the at least one computer systemmay use the scores to formulate a regional or dataset image qualitymetric based on the evaluated features of image quality. The at leastone computer system may use the results of the image quality assessmentas an input to perform modeling or simulation with the patient-specificdata. However, in addition to modeling and simulation ofpatient-specific data, such as blood flow, the image quality metrics maybe used as inputs for any other activities or assessments.

In one embodiment, method 100 may include using the generated metrics toevaluate if imaging data is suitable to achieve a desired simulationaccuracy (step 108). For example, method 100 may include using theresults of the image quality assessment to accept or reject the imagedata for modeling or simulation based on predetermined criteria relatedto accuracy, precision, performance, or other requirements. In addition,method 100 may include using the results of the image quality assessmentto estimate performance metrics (e.g., time to complete analysis, costof analysis) or make a decision based on those metrics to perform or notperform modeling or simulation with the patient-specific data using atleast one computer system. For example, a computer system may computeand display a time to complete analysis, based on the results of theimage quality assessment. In addition, or alternatively, the computersystem may compute and display a cost of analysis, based on the resultsof the image quality assessment. In addition, or alternatively, thecomputer system may display and/or transmit a recommendation orrequirement to perform or not perform modeling or simulation with thepatient-specific data using at least one computer system, based on theresults of the image quality assessment. Any of such computedinformation, such as computed time to complete analysis, cost ofanalysis, and/or perform/not perform analysis may be displayed to aphysician, technician, or any other healthcare provider, whether throughan electronic display and/or over an electronic network.

In accordance with another embodiment, method 100 may include using thegenerated metrics to estimate accuracy or confidence in simulationresults (step 110). For example, method 100 may include using theresults of the image quality assessment to perform modeling orsimulation, and output results with a confidence metric (e.g., errors,percent confidence, confidence intervals, accuracy or precisionestimates) associated with the simulation results.

In accordance with another embodiment, method 100 may include using thegenerated metrics to guide simulation techniques best suited to achievedesired simulation accuracy (step 112). For example, method 100 mayinclude using the results of the image quality assessment to model orsimulate using different techniques or algorithms in the entire imagedata set or relevant affected portions depending on the image qualityassessment to enhance or achieve desired performance, accuracy,precision, or other requirements.

In accordance with another embodiment, method 100 may include using thegenerated metrics to select, combine, or correct best available data toachieve desired simulation accuracy from a plurality of options received(step 114). For example, method 100 may include using the results of theimage quality assessment to correct for image quality issues prior toperforming modeling or simulation, to enhance or achieve desiredperformance, accuracy, precision, or other requirements. In addition oralternatively, method 100 may include using the results of the imagequality assessment to select the dataset from a multitude of availabledata (e.g., alternate series or reconstructions) that is mostappropriate for performing modeling or simulation, to enhance or achievedesired performance, accuracy, precision, or other requirements. Inaddition or alternatively, method 100 may include using the results ofthe image quality assessment to combine various pieces of differentimaging data (e.g., other phases or other reconstructions or modalities)to compensate for image quality issues and perform modeling orsimulation with the patient-specific data using at least one computersystem, to enhance or achieve desired performance, accuracy, precision,or other requirements.

In accordance with another embodiment, method 100 may include using thegenerated metrics to provide feedback to obtain better image quality toachieve desired accuracy (step 116). For example, method 100 may includeusing the results of the image quality assessment to assess or score asingle, various, or combination of features of image quality in atimeframe that allows feedback to be provided to the personnel providingthe imaging data such that they could correct, redo, or update theimaging data to meet some predefined criteria to enhance or achievedesired performance, accuracy, precision, or other requirements. Usingthe updated or corrected image data, the at least one computer systemand method may perform one or more additional iterations of modeling orsimulation.

FIG. 4 is a flow chart that describes an exemplary method 120 forperforming user-guided assessment of image quality, according to anexemplary embodiment. As shown in FIG. 4, in one embodiment, method 120may include receiving patient anatomical image data (step 122). Forexample, step 122 may include obtaining image data 10 at a computersystem 40, consistent with any of the disclosure of FIGS. 1 and 2 above.Method 120 may further include determining one or more centerlines ofpatient vasculature (step 124). For example, step 124 may include usinga processor of computer system 40 to automatically identify one or morecenterlines of patient vasculature, consistent with any of thedisclosure of FIGS. 1 and 2 above. In one embodiment, the processor ofcomputer system 40 may add centerlines to the primary vessels (RCA, LAD,and LCX), or any other vessels greater than 2 mm in diameter.

Method 120 may further include prompting a user to input image qualityissues, image anomalies, image artifacts, or other “regions ofuninterpretability” along each centerline using a set of visual criteria(e.g., blur, motion, image artifacts, etc.) (step 126). For example, aprocessor of computer system 40 may initiate the display of one or moreimages and centerlines, and prompt a user to review and inspect theimages, and to enter inputs of image quality issues upon finding anymisregistration artifacts, blurring, stents, undesirablecontrast-to-noise ratio, motion artifacts, blooming artifacts, calcium,scanner errors, missing slices, incomplete data, and so on. For example,the processor of computer system 40 may generate user interface elementsthat a user can manipulate to indicate that the user identifies any ofthe image quality issues described herein, along with certaincharacteristics of location, quantity, or extent of those issues. In oneembodiment, either the user or the processor of computer system 40 maycharacterize each region of uninterpretability as being either short(e.g., 0-5 mm) or long (e.g., greater than 5 mm).

In one embodiment, users may be prompted to identify contrast timing andnoise as “good” if an image exhibits high contrast, low noise, and mildright contrast; as “marginal” if an image exhibits moderate contrast,noise, and high right contrast; and as “poor” if an image exhibits lowcontrast, high noise, and high right contrast. In one embodiment, usersmay be prompted to identify misregistration as “good” if an imageexhibits no misregistration affecting lumen geometry; as “marginal” ifan image exhibits misregistration artifacts that are nearlyperpendicular to the vessel and can be corrected; and as “poor” if animage exhibits misregistration that cannot be corrected or that existsin an area of disease such that the lumen cannot be determined. In oneembodiment, users may be prompted to identify motion as “good” if themotion does not affect the lumen or plaque; as “marginal” if the imagereflects that the lumen is affected, but the vessel can be interpretedand modeled with assumptions; and as “poor” if the image reflects thatthe lumen interpretability is severely affected by motion. In oneembodiment, users may be prompted to identify blooming as “good” if mildblooming does not affect lumen interpretability; as “marginal” if a highdegree of blooming may require correction but the image still retainslumen visibility; and as “poor” if severe blooming artifact completelyobscures the lumen.

Method 120 may further include receiving or calculating a score for eachregion of uninterpretability based on the length of the region (step128). In an exemplary embodiment, scores for image qualityissues—whether on a qualitative scale (e.g., Likert scale) orquantitative measures—may be determined and analyzed for how they impactor predict modeling and simulation accuracy, precision, and performance.The image quality assessment may have absolute failure criteria in whicha dataset is deemed unacceptable, it may have various metrics that arescored, combined, and weighted over a region, vessel, or entire dataset,or it may have a combination of both. For example, in certainembodiments, an automatic fail may be triggered whenever some single orcombined image quality issue(s) results in 25% or more of an arterybeing indiscernible (whether due to noise, motion, blooming, poorcontrast, misregistration, etc.).

In an exemplary embodiment, metrics may be generated for either a region(e.g., vessel) or dataset by the image quality scoring system and methodbased on ratings of at least some of the image quality issues describedpreviously. In one embodiment, each region of uninterpretability mayreceive a score based on length (e.g., in one embodiment: 1.5 for short,and 3 for long). In one embodiment, a score to reject a patient's images(i.e., a “case”) may involve a score of 6 for a single main vessel, ascore of 8 for an entire case, and/or a so-called “red flag” that hasbeen assigned a score of 10.

FIG. 9 depicts a table of an exemplary rubric for scoring imagecharacteristics based on lumen features of cardiovascular vessels,according to various exemplary embodiments. Specifically, FIG. 9 depictsone exemplary embodiment of a scoring rubric for assigning scores toregions of uninterpretability or other image quality issues. Forexample, as shown in the exemplary rubric of FIG. 9, a different scoremay be assigned to each characteristic (i.e., either combination (noise,motion, contrast), motion, mis-alignment, noise, blooming, contrast, oropacification) based on an amount of region affected (e.g., “full” or“small,” or “long” or “short”), and based on whether the identifiedcharacteristic: (i) completely obliterates the lumen or causes missinginformation and prevents identification of disease; (ii) preventsdetermination of precise lumen boundary, but enables identification ofdisease present (e.g., shows where minimal luminal diameter (“MLD”)would be); or (iii) prevents determination of precise lumen boundary andprevents identification of disease. It should be appreciated that thescoring rubric of FIG. 9 is only an example, and that any alternativescoring mechanisms are contemplated within the scope of this disclosure.For example, the scoring system may be inverted such that lower scoresindicate lower image quality, whereas higher scores indicate higherimage quality. Alternatively or additionally, the scoring system may bebased on an exponential, logarithmic, or fractional scale. Alternativelyor additionally, the scoring system may be generated based on acolor-coded and/or letter-grade scale, where a color and/or letterindicates some quality level of the scored images.

In certain embodiments, the image quality scores may be weighted andcombined with other factors including but not limited to: magnitude ofeffect, size of the issue, regions affected, issue type (e.g., noise ormotion), presence/absence of disease, vessel size, location in theheart, uncertainty in lumen definition, combination with other issues,visual interpretability, algorithm confidence, etc. A function forregions or datasets may be derived that use some, all, or additionalweighting factors. One such example is presented below:Quality_(region) =f(Σ_(i)^(vessel)issue_(i)*magnitude*type*disease*size*vesselsize*location*lumenuncertainty)Quality_(dataset) =f(Σ_(i)^(dataset)issue_(i)*magnitude*type*disease*size*vesselsize*location*lumenuncertainty)

In an exemplary embodiment, limits may be defined for the followingcriteria, and unacceptable scores may result in rejections of data forcoronary blood flow modeling and simulations:

-   -   image resolution: pixel size <0.5 mm    -   slice thickness ≦1.0 mm    -   number of scanner slices ≧64    -   missing slices or missing data not acceptable    -   must have sublingual nitrates at the time of CT acquisition    -   coronary arteries and myocardium must be completely included in        the dataset    -   presence of anatomic abnormalities, such as severe congenital        heart disease, are not acceptable    -   presence of implanted devices, such as pacemakers, or prior        surgeries, such as bypass grafts, are not acceptable

In an exemplary embodiment, the following criteria may be defined at alocal level. For example, for each image quality issue, a score ofmagnitude of the effect may be generated. Other information may beadded, such as the location and size of the issue, based on thefollowing:

-   -   contrast level    -   noise level    -   misregistration or misalignment    -   motion or blurring    -   partial volume effect or blooming    -   general uninterpretable or poorly defined regions

Method 120 may further include calculating and outputting a total scoreof the regions of uninterpretability for the image as a quantitativemetric of image quality (step 130). For example, in one embodiment, thescores for each issue weighted by size and location may be summed overeach vessel and case.

FIG. 5 is a flow chart that describes an exemplary method 150 forperforming computer-automated assessment of medical image quality,generation of image quality metrics, and use of image quality metrics,according to various exemplary embodiments. In one embodiment, method150 may include receiving patient anatomical image data, and generatinga vessel model of patient vasculature (step 152). Method 150 may furtherinclude determining, using a processor, one or more global imageproperties (step 154).

In an exemplary embodiment, the disclosed systems and methods mayinvolve automatically assessing quantitative information that can beextracted from the imaging data including, but not limited to, the imageresolution, slice thickness, reconstruction kernel, number of scannerslices, missing slices or missing data, and phase of acquisition. Theinformation may be extracted by analyzing dimensions or tags in theimage data (e.g., DICOM header). Each of these categories may havesimple accept/reject criteria. The following serve as examplespecifications:

-   -   image resolution: pixel size <0.5 mm    -   slice thickness ≧0.9 mm    -   reconstruction kernel equal to manufacturer specific filters    -   number of scanner slices ≧64    -   missing slices or missing data not acceptable    -   phase of acquisition >65% and ≦80%

In an exemplary embodiment, the resolution, slice, phase, and datacompleteness may not have absolute accept/reject criteria, but rather arange of scores that will contribute to an overall image quality metricfor the dataset. For example, resolution and slice thickness may becombined to obtain a voxel volume (e.g., 0.4 mm×0.4 mm×0.75 mm). Higheror lower resolutions may add or subtract from an overall dataset score.

In an exemplary embodiment, information regarding medicationadministered and heart rate during an imaging study may be submittedwith the study to the computer system. The computer system mayaccept/reject a dataset based on this information, e.g., absence ofsublingual nitrates may necessitate rejection of the dataset.Alternatively, the presence, absence, or dose of medication, the HR, orother physiologic metrics may contribute to the overall score or directthe method and computer system to perform modeling and simulation withdifferent methods. For example, the absence of sublingual nitrates maydirect the use of alternate coronary lumen segmentation algorithms toensure proper vessel sizes.

In an exemplary embodiment, missing anatomic data, presence of anatomicabnormalities, and presence of implanted devices or prior surgeries maybe detected by a user of the computer system. The presence or absence ofthese issues may add to a score or result in accept/reject decisions forthe dataset. These assessments may also be automated.

Method 150 may further include determining, using a processor, one ormore centerlines of patient vasculature based on the vessel model (step156). Method 150 may further include determining one or more local imageproperties at each of a plurality of centerline locations (e.g., blur,motion, contrast, etc.) (step 158). In an exemplary embodiment, thecomputer system may be configured to automatically determine such localimage properties, or local or global image quality, by implementing afully automated quantitative assessment of the image quality, based onany one or more of the image quality issues described herein. Forexample, a processor of computer system 40 may automatically determineone or more local image properties in any of the manners discussed abovewith respect to the user-guided method of FIG. 4, except that computersystem 40 may do so automatically, such as by executing an algorithm, insome cases according to the exemplary concepts described below.

In an exemplary embodiment, the contrast and noise levels may beassessed locally (e.g., at a section of a vessel) or globally (e.g.,across multiple vessels or a large representative vessel or structure).This assessment may be performed by taking measurements of the contrastlevel (e.g., mean contrast in a region of interest) and noise level(e.g., standard deviation of contrast in a region of interest). Thesemeasurements may also be combined to create a signal to noise ratio bydividing the contrast and noise measurements. Additionally, the contrastand noise measurements may take into account background or surroundingtissue contrast and noise to represent the difference between the regionof interest (e.g., coronary artery) and background data (e.g.,myocardium and epicardial fat). Alternatively, the contrast, noise, andcontrast to noise ratio may be assessed qualitatively on a local orglobal scale by rating the degree of noise in comparison to referencestandards (e.g., 1=poor, 2=marginal, 3=good). In one embodiment, aprocessor of computer system 40 may calculate noise based on analgorithm that receives as inputs some CT volume data and aorta maskdata (e.g., from zhf file), and that outputs an aorta mean HounsfieldUnit (“HU”) value, noise standard deviation, surrounding mean HU value,and CNR. In one embodiment, a processor of computer system 40 maycalculate contrast differences between left and right ventricles basedon an algorithm that receives as inputs some CT volume and myomass (longaxis and segmentation), and that outputs LV mean HU value and RV mean HUvalue.

In an exemplary embodiment, misregistration or misalignments may bedetected by searching through the data or globally through the datasetor locally near the arteries to identify where offsets occur betweenadjacent images. These may be detected by a user or by the computersystem. The degree of misregistration may be classified by the distancethe data is shifted, the amount of a region that is affected (e.g.,length of vessel that is affected), or by the orientation of the regionaffected (e.g., perpendicular or parallel to vessel). Alternatively, themisregistration may be assessed qualitatively on a local or global scaleby rating the degree of misregistration in comparison to referencestandards (e.g., 1=poor, 2=marginal, 3=good). In one embodiment, aprocessor of computer system 40 may calculate index-slicemisregistration based on an algorithm that receives a CT image, outputspeaks locations, and scores values.

In an exemplary embodiment, motion or blurring artifact may be detectedby scanning through the global data or locally near the arteries toidentify areas where the image data is blurred or has soft edges (e.g.,the edge of a vessel has soft and smeared edges). These may be detectedby a user or by the computer system. The degree of motion may beclassified by the distance the data blurred, the gradient of the imagedata, or other quantitative means. Alternatively, the motion may beassessed qualitatively on a local or global scale by rating the degreeof motion in comparison to reference standards (e.g., 1=poor,2=marginal, 3=good).

In an exemplary embodiment, partial volume or blooming artifacts may bedetected by scanning through the global data or locally near thearteries to identify areas where the image data contains bright featuresthat interfere with other parts of the data. These may be detected by auser or by the computer system. The degree of blooming may be classifiedby the intensity, the size, and/or a measurement of how far it spreadsinto neighboring structures (e.g., how much does blooming cover thelumen). Alternatively, the blooming may be assessed qualitatively on alocal or global scale by rating the degree of blooming in comparison toreference standards (e.g., 1=poor, 2=marginal, 3=good).

In an exemplary embodiment, beam hardening may be detected by scanningthrough the data globally or locally near the arteries to identify areaswhere the image data contains dark spots or streaks that interfere withother parts of the data. These may be detected by a user or by thecomputer system. The degree of beam hardening may be classified by theintensity, the shape, and/or a measurement of how much it interfereswith neighboring structures (e.g., how much does beam hardening obscurethe lumen). Alternatively, the beam hardening may be assessedqualitatively on a local or global scale by rating the degree of beamhardening in comparison to reference standards (e.g., 1=poor,2=marginal, 3=good).

In an exemplary embodiment, any other general characteristic affectingimage quality may be detected by scanning through the global data orlocally near the arteries to identify areas where the image data is notinterpretable or where feature definition such as the lumen is poor.These may be detected by a user or by the computer system. These may bequantified by the degree to which they affect lumen quality compared toadjacent regions and by the amount affected. Alternatively, thecharacteristic may be assessed qualitatively on a local or global scaleby rating the degree of characteristic in comparison to referencestandards (e.g., 1=poor, 2=marginal, 3=good).

Method 150 may further include predicting local uncertainty in thevessel model based on the local and global image properties (step 160).For example, in certain embodiments, machine learning, regression, andother statistical techniques may be used to derive functions or modelsrelating image quality to modeling, simulation, and performance. Asdescribed in the next section, these metrics may be adjusted to achievedifferent needs.

Method 150 may further include using the local uncertainty prediction tocompute a confidence interval of a simulated blood flow calculation(step 162). Method 150 may further include calculating and outputting atotal uncertainty for the vessel model as a quantitative metric of imagequality (step 164).

Exemplary embodiments of the step (step 162) of using the localuncertainty prediction to compute a confidence interval of a simulatedblood flow calculation will now be described in more detail. In anexemplary embodiment, metrics may be tuned and have various correlationsor criteria associated with them to achieve purposes including but notlimited to: assess sufficiency of data for automated modeling, assesssufficiency of data for user-guided interpretation and/or modeling,direct the method or system used to model data, accept/reject imagingdata, determine which of a multitude of a received data is best formodeling (e.g., alternate phases or reconstructions), provide feedbackon imaging data in order to obtain improved or corrected data, labelresults differently depending on the image quality scores, and provideconfidence estimation depending on the uncertainty associated with theimage quality issues. All of these purposes are within the context ofmeasuring and predicting simulation and modeling accuracy, precision,and performance.

In an exemplary embodiment, criteria may be derived for relating theimage quality metric to error of FFR simulation results versus areference standard of measured FFR. Coronary vessels and/or fulldatasets that pass the criteria may be accepted for processing in orderto ensure a certain level of accuracy and precision of the solution.Vessels or full datasets that fail the criteria may be correlated withhaving higher error than desired. Alternatively, vessels or fulldatasets that fail may be directed to other methods and or systems thatcan achieve higher accuracy.

In an exemplary embodiment, criteria may be derived relating the imagequality metric to variability in simulated FFR results based ondifferent users. Coronary vessels and/or full datasets that pass thecriteria may be accepted for processing in order to ensure a certainlevel of precision of the solution. Vessels or full datasets that failthe criteria may be correlated with having higher variability thandesired. Alternatively, vessels or full datasets that fail may bedirected to other methods and or systems that can achieve higherprecision.

In an exemplary embodiment, criteria may be derived relating the imagequality metric to performance efficiency of modeling and simulatingblood flow. Datasets above certain scores may be rejected, associatedwith a special processing fee, or may be directed to different resourcesto obtain a desired efficiency. Alternatively, an estimate of simulationcost and/or time may be provided based on the image quality score. Forexample, if the image quality is getting worse and worse, it may bepossible to estimate higher cost or price associated with manuallyidentifying and correcting image quality characteristics or anatomiccharacteristics.

In an exemplary embodiment, criteria may be derived to label resultsfrom FFR simulations that are performed in coronary vessels and/or fulldatasets that contain a region of low image quality that fails to meetthe criteria. Such labels may serve to provide indication that there isuncertainty in the solution in that region of the model and/or toexplain what is modeled in light of the uncertainty (e.g., anassumption).

In an exemplary embodiment, criteria may be derived to label regions inmodels that require special processing to ensure accuracy (e.g.,inspection, different algorithm, expert review).

In an exemplary embodiment, criteria may be set to use certain methodsin determining vessel size in the presence of certain image qualityissues. For example, when a blooming artifact around calcified plaque ispresent, methods and systems for determining the lumen boundary (andsubsequently blood flow) near the artifact may differ from those in theabsence of artifact.

In an exemplary embodiment, criteria may be derived to assess theuncertainty or confidence of FFR simulation results that are performedin coronary vessels and/or full datasets that contained a region of lowimage quality that failed to meet the criteria. The uncertainty orconfidence may result in the FFR results being reported with a %confidence or a confidence interval based on the effect of image quality(e.g., FFR is 0.87+/−0.05 or FFR is <0.80 with 76% confidence).

In an exemplary embodiment, criteria may be derived relating the imagequality metric to error of FFR simulation results versus a referencestandard of measured FFR. Coronary vessels and/or full datasets may beranked on their scores against this criteria to determine which of amultitude of data would be best for simulating FFR results and obtainingthe highest accuracy.

In an exemplary embodiment, at least one computer system may be locatedor rapidly accessible from the site where imaging data is created.Criteria may be set to assess the image quality as it relates toimpacting or predicting FFR simulation results. Coronary vessels and/orfull datasets may be ranked on their scores against this criteria toprovide instant feedback such that the site creating the imaging datacould correct or update data until it meets the criteria needed toobtain desirable accuracy. Alternatively, instant feedback could beprovided with an estimate or confidence associated with a reducedaccuracy, allowing the site creating the imaging data to accept a loweraccuracy if there is clinical benefit.

FIG. 6 is a flow chart that describes an exemplary method 200 forassessing medical image quality, generating image quality metrics, andusing image quality metrics, in the context of estimating coronaryfractional flow reserve values, according to various exemplaryembodiments. For example, FIG. 6 depicts a method of estimating coronaryfractional flow reserve (FFR) values based on certain image qualityassessment techniques disclosed herein, and FFR calculation techniquesdescribed in U.S. Pat. No. 8,315,812.

As shown in FIG. 6, method 200 may begin by selecting a particularpatient (step 202) and receiving patient image and physiologic data(step 204). Method 200 may include validating the received imagery anddata with known patient information (step 206), for example, for useridentity or privacy reasons. Method 200 may include determining whetherthe received data is acceptable (step 208). For example, step 208 mayinclude either accepting or rejecting each of one or more receivedimages based on any one or combination of the image assessment andscoring techniques disclosed herein. If any one or more images arerejected, method 200 may include generating a rejection report (step210). For example, the method may include obtaining images that arerejected and providing feedback to the user on the rejected images, toassist the user in obtaining new images that would not be rejecting. Forexample, the computer system may send to a technician, physician, or anyother healthcare professional, a report of rejected images along withguidelines and/or recommendations for adjusting image acquisitionparameters that would enable obtaining images of higher image qualityscores. Such reports, rejected images, guidelines, and/orrecommendations may be displayed to a physician, technician, or anyother healthcare provider, whether through an electronic display and/orover an electronic network.

If the data is acceptable, then method 200 may include performingradiologic workstation pre-processing and assessment of data (step 212).Method 200 may then include calculating myocardial mass (step 214),generating an initial tree of coronary vessels and branches (step 216),finding one or more vessel lumen edges (step 218), matching thresholdsin main vessels to edge segmentations (step 220), and detecting,segmenting, removing, and smoothing any plaque or calcium (step 222).Method 200 may then include determining whether segmentation succeededusing an algorithm (step 224). If not, then method 200 may includemanually segmenting or correcting the segmentation (step 226). Ifsegmentation succeeded, then method 200 may include determining whetherindependent verification of segmentation is acceptable (step 228). Ifnot, then method 200 may return to step 216 of generating the initialtree of coronary vessels and branches.

If segmentation was acceptable (step 228; Yes), then method 200 mayinclude outputting and smoothing a solid model (step 230), extractingone or more vessel centerlines (step 232), calculating vesselcross-sectional areas (step 234), trimming the model (step 236),generating a solid model (step 238), setting boundary conditions forhyperemia conditions (step 240), and generating a final mesh (step 242).Method 200 may then include verifying whether mesh and boundaryconditions are acceptable (step 244). If not, then method 200 may returnto step 234 of calculating cross-sectional area.

If mesh and boundary conditions are acceptable (step 244; Yes), thenmethod 200 may include simulating hyperemia flow (step 246) andverifying whether the solution result is acceptable (step 248). If not,then method 200 may include refining stenosis geometry, correctingboundary conditions, etc. (step 258). If the solution result isacceptable (step 248; Yes), then method 200 may include extracting cFFR,documenting the same along the vessels, and generating images for areport (step 250). Method 200 may then include determining whetherindependent verification of the final results is acceptable (step 252).If not, then method 200 may include returning to step 216 of generatingthe initial tree of coronary vessels and branches.

If the independent verification of the final results is acceptable (step252; Yes), then method 200 may include finalizing a report (step 254)and forwarding the finalized report to the physician (step 256).

FIG. 7A is an exemplary box plot of fractional flow reserve error andacceptance or rejection based on CT image quality review, according tovarious exemplary embodiments. For example, the box plot of FIG. 7A mayillustrate that rejected cases may have more variation of FFRct errorthan do the accepted cases.

FIG. 7B is an exemplary box plot of fractional flow reserve error andscoring based on CT image quality review, according to various exemplaryembodiments. For example, the box plot of FIG. 7B may illustrate thatvariation of FFRct error may increase as an image quality penalty scoreincreases. In other words, cases having a score between 7 and 10 mayhave substantially higher variation in FFRct error than cases having ascore between 0 and 1.

FIG. 8 is an exemplary bar graph depicting comparisons between qualityof fractional flow reserve and computed tomography based on imagequality by number of vessels, according to various exemplaryembodiments. Specifically, the bar graph of FIG. 8 may depictperformance as correlated to vessel-specific quality ratings of CTinterpretability.

FIG. 10 is an exemplary screenshot depicting computed tomography qualityratings vs. benchmark performance, according to various exemplaryembodiments. For example, as shown in FIG. 10, a processor of computersystem 40 may provide a user interface by which a user, such as an imagetechnician, physician, or any other health care provider, may review aCT image quality assessment for each of a plurality of patients or“cases.” As shown in FIG. 10, in one embodiment, each case may bedisplayed as having one of “Good,” “Marginal,” or “Poor” image quality.The image quality may also be compared, such as by display in a bar orother chart, with certain benchmark performance standards of imagequality. The interface may generate and display quality reports, and/ormake recommendations for improving the obtained quality. By way ofexample, if a high level of noise is detected, the user interface maysuggest reviewing the “mA/kV settings,” and may provide a link to atutorial on noise. Of course, the user interface and guidelines mayprovide the user with an assessment of image quality and recommendationsfor improving image quality in relation to any of the image qualityissues discussed herein.

The presently disclosed systems and methods may enable the automaticestimation and correction of image quality issues, thereby reducinghuman time and variability before now associated with quality controlreview of image data. Moreover, the presently disclosed systems andmethods may provide better understanding of a relationship betweensimulation and modeling accuracy (e.g., FFR error) and image qualityscores. Still further, the presently disclosed systems and methods mayenable users to better and automatically select a desirable base phaseof image for analyst review, and provide better “red flags” for furtherreview or rejection of certain scans.

In one embodiment, the presently disclosed techniques may includedefining input uncertainties, calculating FFR analysis sensitivities,and calculating confidence intervals in FFR, according to any of thetechniques described in U.S. application Ser. No. 13/864,996, filed Apr.17, 2013, the entirety of which is incorporated herein by reference.

In one embodiment, the presently disclosed techniques may includeperforming any of the various presolving techniques described in U.S.application Ser. No. 13/625,628, filed Sep. 24, 2012, the entirety ofwhich is incorporated herein by reference.

In one embodiment, FFR values may be obtained using machine learningestimates as opposed to physics-based simulations. In other words,instead of executing a coronary solver, such as in the '812 patent, foreach of the plurality of collocation points, the disclosed systems andmethods may efficiently estimate blood flow characteristics based onknowledge gleaned from analyzing blood flow of numerous other patients.For example, the disclosed systems and methods may include performingany of the various machine learning techniques described in U.S.Provisional Patent Application No. 61/700,213, filed Sep. 12, 2012, theentirety of which is incorporated herein by reference. Thus, in oneembodiment, FFR values may be obtained by training a machine learningalgorithm to estimate FFR values for various points of patient geometrybased on feature vectors of patient physiological parameters andmeasured blood flow characteristics, and then applying the machinelearning algorithm to a specific patient's geometry and physiologicalparameters to obtain predicted FFR values.

One or more of the steps described herein may be performed by one ormore human operators (e.g., a cardiologist or other physician, thepatient, an employee of the service provider providing the web-basedservice or other service provided by a third party, other user, etc.),or one or more computer systems used by such human operator(s), such asa desktop or portable computer, a workstation, a server, a personaldigital assistant, etc. The computer system(s) may be connected via anetwork or other method of communicating data.

Any aspect set forth in any embodiment may be used with any otherembodiment set forth herein. Every device and apparatus set forth hereinmay be used in any suitable medical procedure, may be advanced throughany suitable body lumen and body cavity, and may be used for imaging anysuitable body portion.

Various modifications and variations can be made in the disclosedsystems and processes without departing from the scope of thedisclosure. Other embodiments will be apparent to those skilled in theart from consideration of the specification and practice of thedisclosure disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the disclosure being indicated by the following claims.

What is claimed is:
 1. A method for automatically selecting image(s) toproduce model(s) for a patient-specific simulation of blood flow, usingat least one computer system, the method comprising: receiving one ormore images of at least a portion of a patient's anatomy; obtaining oneor more image properties of the received images using one or moreprocessors of the at least one computer system; obtaining, for aselected one of the received images, an identification of one or moreimage characteristics of the selected one of the received images, wherethe one or more image characteristics are associated with an anatomicfeature of the patient's anatomy; identifying a type of patient-specificsimulation to perform using the received images; receiving, for the typeof patient-specific medical simulation using the received images, aselected level of simulation accuracy, precision, or performance;quantifying a minimum image quality to render the received selectedlevel of simulation accuracy, precision, or performance; generating animage assessment based on the quantified minimum image quality, and theone or more image properties or image characteristics; and applying theimage assessment to each of the received images to select one or moreimages to produce one or more models for a patient-specific simulationof blood flow.
 2. The method of claim 1, further comprising: combiningor correcting one or more of the selected images to generate a model fora patient-specific simulation of blood flow.
 3. The method of claim 1,further comprising: guiding simulation techniques based on the selectedlevel of accuracy, precision, or performance.
 4. The method of claim 1,further comprising: performing, using a processor of the computersystem, a patient-specific simulation of blood flow using one or more ofthe selected images.
 5. The method of claim 4, further comprising:performing anatomic localization of the at least a portion of thepatient's anatomy based on the received images, wherein the simulationis based on the anatomic localization of the at least a portion of thepatient's anatomy.
 6. The method of claim 1, further comprising:estimating performance metrics based on time to complete a simulation orcost of a simulation, wherein the image assessment is based on theestimated performance metrics.
 7. The method of claim 1, furthercomprising: calculating an image quality score based on the one or moreimage properties or the one or more image characteristics, wherein theimage assessment is based on the image quality score.
 8. The method ofclaim 1, wherein the one or more image characteristics include one ormore of: a local contrast level, a local noise level, a misregistration,a misalignment, a local motion anomaly, a local blurring anomaly, apartial volume effect, a blooming effect, and an artifact.
 9. The methodof claim 1, wherein the one or more image properties include one or moreof: an image resolution, a medical anatomy slice thickness, a number ofscanner slices, a medication parameter, and a patient characteristic.10. The method of claim 1, further comprising: generating andtransmitting a rendering of a recommendation to perform or not performpatient-specific medical simulation based on the image assessment.
 11. Asystem for automatically selecting image(s) to produce model(s) for apatient-specific simulation of blood flow, the system comprising: adigital storage device storing instructions for assessing the quality ofmedical images of at least a portion of a patient's anatomy; and aprocessor configured to execute the instructions to perform a methodincluding: receiving one or more images of at least a portion of apatient's anatomy; obtaining one or more image properties of thereceived images using one or more processors of the at least onecomputer system; obtaining, for a selected one of the received images,an identification of one or more image characteristics of the selectedone of the received images, where the one or more image characteristicsare associated with an anatomic feature of the patient's anatomy;identifying a type of patient-specific simulation to perform using thereceived images; receiving, for the type of patient-specific simulationusing the received images, a selected level of accuracy, precision, orperformance; quantifying a minimum image quality to render the receivedselected level of accuracy, precision, or performance; generating animage assessment based on the quantified minimum image quality, and theone or more image properties or image characteristics; and applying theimage assessment to each of the received images to select one or moreimages to produce one or more models for a patient-specific simulationof blood flow.
 12. The system of claim 11, wherein the processor isfurther configured for: combining or correcting one or more of theselected images to generate a model for a patient-specific simulation ofblood flow.
 13. The system of claim 12, wherein the processor is furtherconfigured for: guiding simulation techniques based on the selectedlevel of accuracy, precision, or performance.
 14. The system of claim11, wherein the processor is further configured for: performing, using aprocessor of the computer system, a patient-specific simulation of bloodflow using one or more of the selected images.
 15. The system of claim14, wherein the processor is further configured for: performing anatomiclocalization of the at least a portion of the patient's anatomy based onthe received images, wherein the simulation is based on the anatomiclocalization of the at least a portion of the patient's anatomy.
 16. Thesystem of claim 11, wherein the processor is further configured for:estimating performance metrics based on time to complete a simulation orcost of a simulation, wherein the image assessment is based on theestimated performance metrics.
 17. The system of claim 11, wherein theprocessor is further configured for: calculating an image quality scorebased on the one or more image properties or the one or more imagecharacteristics, wherein the image assessment is based on the imagequality score.
 18. The system of claim 11, wherein the one or more imagecharacteristics include one or more of: a local contrast level, a localnoise level, a misregistration, a misalignment, a local motion anomaly,a local blurring anomaly, a partial volume effect, a blooming effect,and an artifact.
 19. The system of claim 11, wherein the one or moreimage properties include one or more of: an image resolution, a medicalanatomy slice thickness, a number of scanner slices, a medicationparameter, and a patient characteristic.
 20. A non-transitory computerreadable medium for use on at least one computer system containingcomputer-executable programming instructions for automatically selectingimage(s) to produce model(s) for a patient-specific simulation of bloodflow, that when executed by the at least one computer system, cause theperformance of a method comprising: receiving one or more images of atleast a portion of a patient's anatomy; obtaining one or more imageproperties of the received images using one or more processors of the atleast one computer system; obtaining, for a selected one of the receivedimages, an identification of one or more image characteristics of theselected one of the received images, where the one or more imagecharacteristics are associated with an anatomic feature of the patient'sanatomy; identifying a type of patient-specific medical modeling orsimulation to perform using the received images; receiving, for the typeof patient-specific medical modeling or simulation using the receivedimages, a selected level of accuracy, precision, or performance;quantifying a minimum image quality to render the received selectedlevel of accuracy, precision, or performance; generating an imageassessment based on the quantified minimum image quality, and the one ormore image properties or the image characteristics; and applying theimage assessment to each of the received images to select one or moreimages to produce one or more models for a patient-specific simulationof blood flow.